Event-Triggered Model-Free Adaptive Load Frequency Control for Power Systems With EVs Under Deregulation Environment

As renewable energy sources and electric vehicles (EVs) gradually integrate into the power system, modern grids have evolved into complex large-scale networked control systems. The increasing complexity of the system internal structure presents greater challenges for load frequency control (LFC). To address these challenges, this paper proposes an enhanced model-free adaptive control (MFAC) approach based on system operating data. Initially, this study constructs a dynamic linearized relationship between system output, input and wind power. Then, leveraging pseudo-partial derivatives (PPD) of the I/O data and the partial derivatives of system output with respect to wind power data, a data-driven MFAC algorithm is designed using observer techniques. Given that additional communication burdens may result from data-driven methods, this paper introduces an event-triggered mechanism based on the system output saturation to reduce communication bandwidth. The theoretical analysis thoroughly outlines the design process of the proposed control algorithm and rigorously proves the system stability. Finally, multiple sets of experiments are conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that the event-triggered MFAC (ETMFAC) algorithm effectively mitigates the impact of load disturbances on the power system, ensuring frequency stability. Sensor fault experiments are also carried out to further evaluate the algorithm’s robustness.